Computer aided differential diagnosis of diseases has become important to such fields as cardiology, radiology, and areas of medicine using ultrasonography. These have the benefit of images or time dependent signals. In these fields, differential diagnosis of disease permits the health care provider to more accurately distinguish between many diseases that can produce similar radiographs, electrocardiograms or sonographs (i.e., signals or images). Differential diagnosis, in one embodiment of this invention using neural networks, is applied in the detection ol colon cancer. The present invention pertains to diagnosis of colorectal cancer by analyzing a combination of biological indicators, markers, obtained from body tissues or fluids (including biological waste excretions). The set of markers used are chosen from cancer markers determined to be associated with colorectal cancer. The patient's biological samples are assayed across a set of markers. This analysis yields a set of input data which is processed using a non-linear logic analysis technique known as a neural network. This technique permits healthcare professionals to assess (1) whether the patient is at risk of developing colorectal cancer; (2) whether the patient has colorectal cancer; (3) the state of the patient's disease; (4) what medical treatment would be effective on that patient's disease; and/or (5) the efficacy of a particular treatment modality on the individual patient's disease (surgery, chemotherapy, radiation or a combination of modalities).
For most forms of cancer, if the site is not readily ascertainable to the physician, disease detection frequently occurs when the disease has progressed beyond a state where medical intervention is most beneficial to the patient. For colorectal cancer, currently available means of cancer detection are highly invasive and/or particularly expensive. These methods include exploratory surgery, sigmoidoscopy, colonoscopy, biopsy and internal imaging techniques such as CAT scans and MRI (magnetic resonance imaging).
The scientific discovery that cancer cells exhibit certain proteins, ribonucleic-(RNA) and deoxynucleic acids (DNA), commonly known as biological markers, led to a surge of hope in the biomedical community that a less non-invasive means of detecting cancer would soon be available. Unfortunately, the majority of cancer markers are shared among widely disparate forms of cancer. Furthermore, analysis of markers from biological fluids, for example urine and blood, have resulted in low accuracy with respect to cancer detection.
This invention is a method and apparatus for sensing and classifying a condition of interest in the presence of poor or unknown statistical relationships, in this instance, colorectal cancer. The parameter representative of the condition of interest, a biological marker found to be statistically associated with colorectal cancer, is sensed and an electrical signal or measure representative of the sense parameter is produced. The electrical signal is converted into a digital signal; this digital signal contains a measure representative of the condition of interest and having an unknown or fuzzy relationship. The digital signal is input to an artificial neural network which enhances the relationship to cancer occurrence. It is this unclear or ambiguous correlation that has prevented biological markers for purposes of cancer detection from being efficacious. The digital signals of selected markers are input to an artificial neural network which filters out the "background noise" to produce a filtered output signal from the digital signals, and classifies the output signal of interest from the filtered signal to produce an output representative of the determined signal. In the embodiment described herein, the differential diagnosis of cancer for purposes of diagnosis, prognosis and risk evaluation of developing cancer can all be derived through processing marker data in the neural network system.
An artificial neural network conceptually has several neuron elements (units) and connections between them. These units are categorized into three different layers or groups according to their functions. A first layer defined as an input layer receives the data entered into the system. A second layer defined as the output layer delivers the output data representing an output pattern. A third set of units comprises a number of intermediate layers, also known as hidden layers, that convert the input pattern into an output. This novel method of diagnosing colorectal cancer inputs clinical parameters (such as age, sex, weight, etc.) and marker data into the neural network.
Colon cancer is one of the leading causes of cancer death in the United States, with approximately 60,000 attributable deaths annually. Silverberg et al., 39 CANCER 3 (1989). Scientific evidence suggests that the majority of colon cancers arise from the evolution of normal muscosa progressing into adenomas and finally to adenocarcinomas. Morson, 5 Clin. GASTROENTEROLOGY 505 (1976). Adenoma removal correlates with a reduced risk of rectal carcinomas; analogously, the removal of adenomatous polyps, reduces the likelihood of colon cancer, see Winawer et al., 100 GASTROENTEROLOGY A410 (1991).
Disease diagnosis based on biochemical analysis of a patient's tissue and biological fluid samples is basic to modern medicine. Occasionally, the presence of a single biological substance or marker within a biological sample is sufficient for the determination of a particular disease. Unfortunately single marker cancer detection has proven unreliable, especially when attempting to detect cancer at is early stages. Oftentimes, when cancer detection is indicated by the presence of a single marker, the disease has reached an advanced state and the patient has a correspondingly poor prognosis. As a result, a test has long been sought that is both non-invasive as well as efficacious in the diagnosis of colon cancer.
Historically, colorectal cancer risk prediction has been hindered due to the cancer site and the difficulty in accurately assessing the surrounding tissue for cell abnormalities. Furthermore, cancer risk prediction requires correlating the relationship not only of biological factors such as markers, but also additional diverse clinical factors such as race, sex, family history, environmental actors, and prior colorectal polyp development (if any). The invention can correlate all this data and is beneficial in several ways. First, non-invasive procedures such as phlebotomy decrease the cost to the patient for testing and as a direct result becomes more widely accessible to the general population at a reduced cost. The invention herein also increases the accuracy and efficacy over existing testing procedures by assessing data with relationship to other factors.
Currently, most techniques for screening patients for developing or developed cancer are highly invasive and/or particularly expensive, for example exploratory surgery, biopsy, and internal imaging techniques such as CAT scan and NMR imaging. The present invention is less invasive than surgery or biopsy and relatively inexpensive by comparison, requiring only the taking of fluid samples such as a blood sample, analyzing that sample for particular markers using known techniques, and then processing the marker analysis data using the method of the present invention.
Many of the proteins and protein fragments (including their corresponding genes) present within the body have been shown to act as helpful indicating markers in the diagnosis of disease. A single protein marker does not represent a disease state precisely, however. Several proteins or their gene equivalents more accurately define the disease state, as cancer onset and progression occurs over multiple stages or steps. There will be some association to the individual proteins which act as good markers for a particular disease; there is no totally unique marker to a disease while several of the good markers (in combination) indicate the presence of the disease or a high risk that the disease will soon develop. Another problem with the use of a single marker to test the disease presence is that certain markers are common to more than one disease, introducing the probability of an incorrect diagnosis. Therefore, using a combination of markers increases the accuracy of disease diagnosis and reduces ambiguity.
Proteins and/or their corresponding genes are present in the body at all times. The concentration of gene products, such as proteins or ribonucleic acid (RNA) can determine whether a patient is healthy or diseased. Protein concentrations generally do not conform to a statistically normal distribution (or linear mathematical functions) between individuals. Since real world phenomena are often non-linear, the application of non-linear logic techniques to a predetermined combination of protein marker concentrations provides the best hope to determine the correlation between the various markers and marker combinations and the diagnosis of the disease in question.
U.S. Pat. No. 4,338,811 to Miyagi, et al., issued Jul. 13, 1982 describes a method and apparatus for diagnosis of disease. A two-dimensional pattern diagram representing the relation between integrated values of peaks and the retention times in a chromatogram of substances in a body fluid of a subject person is spatially compared with a two-dimensional pattern representing the same relationships for both normal and diseased persons. Siguel et al., in U.S. Pat. No. 5,075,101, issued Dec. 24, 1991, discloses a method for diagnosis of fatty acid or lipid abnormalities. It discloses a disease diagnostic method for lipid and fatty acid biochemical status; and analytically comparing patterns or domains obtained from indices of the subject with similar indices derived from tissues of subjects with normal and abnormal biochemistry.
Moses E. Cohen et al., in "Use of Pattern-Recognition Techniques to Analyze Chromatographic Data", Journal of Chromatography, Vol. 384, pp. 145-152 (1987) describes the use of decision making algorithms, often denoted expert systems, for the analysis of chromatographic data. In particular, a pattern recognition technique was established using a new class of orthogonal polynomials developed by Cohen. The technique is based on a supervised learning approach, and allows classification of data into two or more categories. In the paper, the usefulness of the technique in the analysis of chromatographic data is illustrated by its application to the diagnosis of bacterial infection of patients with liver disorders by the use of chromatograms obtained from ascetic fluid taken from the patients. Subsequently, in "Medical Diagnosis and Treatment Plans Derived from a Hybrid Expert System," Hybrid Architectures for Intelligent Systems, Abraham Kandel and Gideon Langholz, eds. CRC Press, Boca Raton, Fla., 1992, pp. 330-344, D. L. Hudson, et al. describe the use of neural network approaches in diagnosing metastatic melanoma from chromatographic analysis samples of urine.
Further work by Cohen et al. pertaining to use of Neural networks in the diagnosis of disease is described in "Neural Network Approach to Detection of Metastatic Melonoma from Chromatographic Analysis of Urine", from the Proceedings Annual Symp. Computer Appl. Med. Care, pp. 295-299 (1991). In these proceedings, Cohen et al. discuss in detail the melanogens present in the urine of patients with metastatic melanoma, the constituents of which were used to develop clinical correlations, and the neural networks model used to develop a prospective decision aid which can be used by the clinician as a good indicator of the current state of metastatic disease in each patient.
Dr. William G. Baxt describes the "Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction" in the Annals of Internal Medicine, Vol. 115, pp. 843-848 (1991). An artificial neural network was trained to diagnose, with a high degree of accuracy, acute myocardial infarction in patients presenting to an emergency department. The neural network structure included a "back propagation" algorithm of the kind commonly used in neural network software, to determine the "weights" applied to the input data types and hidden layer variables within the network. This algorithm is used to minimize error in network output, i.e., to minimize the difference between the network output for a specific training pattern and the expected output of that training pattern. The data input to the neural network were selected from the patient presenting symptoms, the past history findings, and the physical and laboratory findings of patients presented with anterior chest pain.
Peter M. Ravdin et al., in "A demonstration that breast cancer recurrence can be predicted by neural network analysis", Breast Cancer Research and Treatment, Vol. 21, pp. 47-53 (1992), describe the use of neural network analysis to successfully predict the clinical outcome of node-positive breast cancer patients. During training, the network received as input information tumor hormone receptor status, DNA index and S-phase determination by flow cytometry, tumor size, number of axillary lymph nodes involved with tumor, and age of the patient, as well as length of clinical followup, relapse status, and time of relapse.
John N. Weinstein et al. describe "Neural Computing in Cancer Drug Development: Predicting Mechanism of Action", in Science, Vol. 258, pp. 447-451 (Dec. 16, 1992). Neural networks are described as being capable of predicting a drug's mechanism of action from its pattern of activity against a panel of 60 malignant cell lines in the National Cancer Institute's drug screening program.
In accordance with the present invention, a connectionist, non-linear logic analytical technique has been used in combination with specific biological indicators (markers) obtained from body tissue or fluids to determine whether a patient has a colorectal cancer or is at risk sufficiently toward developing colorectal cancer that the probability of formation of a cancer is likely. A neural network diagnosing colorectal cancer using biological indicator input was developed, the network comprises:
a) an input layer having a set of marker inputs; PA1 b) at least one hidden layer, wherein each hidden layer has at least five processing elements; and PA1 c) an output layer having at least one output.
The preferred neural network comprises an input layer having about four to ten marker inputs; a number of hidden layers ranging from about 2 to about 15, wherein the total number of processing elements included in the hidden layers ranges from about 24 to about 100; and an output layer having at least 2 outputs.
A neural network of the kind described in combination with markers selected from biological indicators known to have a significantly better relationship with colorectal cancer to serve as a significant indicator of the development of or presence of such cancer. The biological indicators, referred to as markers, are in and selected from the group of markers and include: Carcinoembryonic Antigen (CEA); Alpha-Fetoprotein Modified or Increased Analytical Precision (AFP); Pancreatic Oncofetal Antigen (POA); Antigen Specific for #1116-N5'-19-9 Antibody; Lipid-Bound Sialic Acid (LSA); New oncogenes; Myc oncogenes; Ras oncogenes; Centocor CA 72/4 (a measurement of tumor-associated Glycoprotein 72 (TAG-72) using epitope-specific antibody # B72-3); Antibodies for the p53 gene; Antibodies to Laminin -P.sub.1 ; Yale Col. Sr. Factor; Harvard Uninary Gonadotropin Peptide (UGP); Tumor Suppressor Gene p53; and antibodies to the markers listed in cases where the marker is not an antibody. The DNA and DNA fragment precursors of a protein in addition to the protein can be used as markers. Glycoproteins, lipoproteins and flycolipids frequently are biological indicators of abnormal cell growth.